MPPCEDE: Multi-population parallel co-evolutionary differential evolution for parameter optimization. (15th January 2021)

• Record Type: Journal Article
• Authors: Song, Yingjie; Wu, Daqing; Deng, Wu; Gao, Xiao-Zhi; Li, Taiyong; Zhang, Bin; Li, Yuangang
• Is Part Of: Energy conversion and management. Volume 228(2021)
• Publication Date: 2021-01-15
• Subjects: Photovoltaic -- Parameter extraction -- Equivalent circuit model -- Differential evolution -- Parallel co-evolution -- Multi-population; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2020.113661
• Languages: English
• ISSNs: 0196-8904

Abstract

Graphical abstract: Abstract: In this paper, a novel multi-population parallel co-evolutionary differential evolution, named MPPCEDE, is proposed to optimize parameters of photovoltaic (PV) models and enhance conversion efficiency of solar energy. In the MPPCEDE, the reverse learning mechanism is employed to generate the initial several subpopulations to enhance the convergence velocity and keep the population diversity. A new multi-population parallel control strategy is developed to maintain the search efficiency in subpopulations. The co-evolutionary mutation strategy with elite population and three mutation strategies is proposed to reduce computing resources and balance the exploration and exploration capability through the cooperative mechanism, improve the convergence speed, realize the information exchange. Then the MPPCEDE is employed to effectively optimize parameters of PV models under various conditions and environments to obtain a parameter values of PV models. Finally, the effectiveness of the proposed method is tested by different PV models and manufacturer's datasheet. The experimental and comparative results demonstrate that the MPPCEDE exhibits higher accuracy and reliability, and has fast convergence speed by comparing with several methods in extracting parameters of PV models. Highlights: A multi-strategy-based co-evolutionary DE algorithm(MPPCEDE) is proposed. A new multi-population parallel co-evolutionary mutation strategy is proposed. The reverse learning mechanism is used to initialize the population to keep population diversity. An adaptive adjustment strategy of parameters is designed to improve optimiztaion performance. Comprehensive experiments are designed to evaluate the effectiveness of MPPCEDE.